Systems that locate, track, and monitor the status of personnel and/or assets generally utilize or incorporate known technology including, for example, Global Positioning System (GPS) technology, inertial and non-inertial sensor devices, and signal analysis methods. A variety of factors, however, can negatively impact the accuracy of such systems.
For example, although GPS has proved to be a useful navigation and tracking tool for outdoor tracking applications, a number of limitations exist when applying GPS to indoor navigation and tracking GPS relies primarily on a line of sight signal acquisition. In indoor environments and in close proximity of most complex buildings, however, the line of sight of GPS satellites may be substantially obscured and GPS signals may be highly attenuated. As a result, GPS signals are typically several orders of magnitude weaker in indoor environments than outdoors. With such weakened signals, GPS receivers have difficulty receiving GPS signals and calculating accurate position information.
As another example, inertial tracking systems typically use readings from sensors such as gyroscopes and accelerometers to estimate the relative path of personnel and/or assets. Inertial systems, however, may accumulate large errors over time due to factors such as drift in sensor offsets, sensitivity, and measurement limitations of the sensors, as well as limitations of the location determining methods (e.g., algorithms) implemented by such systems. Additionally, the size and cost requirements to track personnel and/or smaller assets may necessitate the use of less expensive and robust inertial sensors, potentially increasing drift in the system. While some man-made assets such as cars and robots use known motion models to aid in their tracking, the apparent lack of a comprehensive model that captures and describes the complexity of human locomotion can further add to inertial errors while tracking personnel.
Signal analysis methods that use signals of the same (or different) frequencies from different reference points to compute the location of personnel and/or assets may be unfeasible due to the need to install a number of reference devices at a particular tracking location (or scene), and may further have large instantaneous errors, and outliers, due to the multi-path effects of signals traveling through various building materials.
As yet another example, while the use of magnetic field sensors and compasses may provide an accurate detection of a heading angle in the absence of magnetic interference, data acquired from these devices may often be inaccurate in buildings due to interference from the building structure, electric lines, and other local magnetic sources. As such, valid compass angles, though available at some building locations, often cannot be depended on at each point in time over a data collection time interval.
These and other drawbacks exist with known tracking systems.